Video Analysis and Generation via a Semantic Progress Function

• URL: http://arxiv.org/abs/2604.22554v1
• Date: Fri, 24 Apr 2026 13:48:51 GMT
• Title: Video Analysis and Generation via a Semantic Progress Function
• Abstract summary: We introduce a one-dimensional representation that captures how the meaning of a given sequence evolves over time.<n>For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence.<n>Building on this insight, we propose a semantic linearization procedure that re parameterizes the sequence so that semantic change unfolds at a constant rate.
• Score: 75.07700965101854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequence evolves over time. For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence. Departures of this curve from a straight line reveal uneven semantic pacing. Building on this insight, we propose a semantic linearization procedure that reparameterizes (or retimes) the sequence so that semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. Beyond linearization, our framework provides a model-agnostic foundation for identifying temporal irregularities, comparing semantic pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing.

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